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45 lines
1.2 KiB
Python
45 lines
1.2 KiB
Python
from mmengine.config import read_base
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from opencompass.models import ByteDance
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from opencompass.partitioners import NaivePartitioner
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from opencompass.runners.local_api import LocalAPIRunner
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from opencompass.tasks import OpenICLInferTask
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with read_base():
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# from opencompass.configs.datasets.collections.chat_medium import datasets
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from opencompass.configs.summarizers.medium import summarizer
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from opencompass.configs.datasets.ceval.ceval_gen import ceval_datasets
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datasets = [
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*ceval_datasets,
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]
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models = [
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dict(
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abbr='skylark-pro-public',
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type=ByteDance,
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path='skylark-pro-public',
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accesskey='xxxxxxx',
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secretkey='xxxxxxx',
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url='xxxxxx',
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generation_kwargs={
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'temperature': 0.7,
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'top_p': 0.9,
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'top_k': 0,
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},
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query_per_second=1,
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max_out_len=2048,
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max_seq_len=2048,
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batch_size=8),
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]
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infer = dict(
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partitioner=dict(type=NaivePartitioner),
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runner=dict(
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type=LocalAPIRunner,
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max_num_workers=2,
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concurrent_users=2,
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task=dict(type=OpenICLInferTask)),
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)
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work_dir = 'outputs/api_bytedance/'
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